Envisioning Neighborhood Futures After the Chinatown Stitch
Aki Di Sandro, Ray Ma, Luming Xu, Sijia Zhang, Yixuan Zhou
2025-03-29
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE, cache = FALSE, fig.show = "asis",
fig.align = "center")
options(scipen=999)
library(sf)
library(ggplot2)
library(tidycensus)
library(tidyverse)
library(ggtext)
library(glue)
library(leaflet)
library(mapview)
library(patchwork)
library(lwgeom)
library(FNN)
library(kableExtra)
library(corrplot)
library(htmltools) # For div() function
library(knitr) # insert images
source("https://raw.githubusercontent.com/urbanSpatial/Public-Policy-Analytics-Landing/master/functions.r")# Change to your own workspace
# knitr::opts_knit$set(root.dir = "E:/Spring/Practicum/DataAnalysis/Chinatown") # Luming
# wd <- "E:/Spring/Practicum/DataAnalysis/Chinatown"
# Aki
knitr::opts_knit$set(root.dir = "~/Documents/MUSA/Spring25/MUSA8010_Practicum/Chinatown/")
wd <- "~/Documents/MUSA/Spring25/MUSA8010_Practicum/Chinatown/"
nhoods_4326 <-
# st_read("DataWrangling/data/philadelphia-neighborhoods.geojson") %>% # Luming
st_read("data/philadelphia-neighborhoods.geojson") %>% # Aki
st_transform('EPSG:4326')
# philly properties (from Sijia)
# from the last 5 years, inflation-adjusted prices, total area > 100 sq ft, prices > 30,000, outliers above 5 SD from the mean were removed
philly_properties <- st_read("data/property_philly_cleaned.geojson") # Aki
# philly_properties <- st_read("DataWrangling/data/property_philly_cleaned.geojson") # Luming1 Use Case
1.1 Background
It is hard to talk about the history of the Philadelphia Chinatown without the construction of I-676, also known as the Vine Street Expressway. Although the highway serves as a significant thoroughfare through Center City Philadelphia, the indispensable repercussion on the adjacent neighborhoods still lingers today. Consequences of the highway such as demolishment of buildings, pedestrian safety threats, traffic congestion, as well as noise and air pollutions had been harming Chinatown and its surrounding neighborhoods over the past decades and are yet to be addressed.
Standing in opposition to the issues, City of Philadelphia Office of Transportation and Infrastructure and Sustainability (OTIS) brought forth the plan of capping sections of the expressway as a solution to address its historical harms on the Chinatown region. This “cap”, named the Chinatown Stitch project, aimed to create a safe and equitable green space for the public that would reconnect areas north and south to the Vine Street expressway. Receiving funds from the US department of Transportation and the local organizations, the construction of the Chinatown Stitch will start in 2027 and is expected to be completed in early 2030.
This purpose of this study is to help OTIS understand the housing market of Philadelphia Chinatown region before and after the implementation of the Chinatown Stitch. The first part of the study will focus the effects of highways on sales prices in the study region as well as the entire Philadelphia. House prices will then be estimated using three predictive models under three different future scenarios: business as usual without the implementation of the Chinatown Stitch, business as usual with the implementation of the Chinatown Stitch, and an alternative future in which the Chinatown Stitch significantly reshapes the urban landscape of the study region.
1.2 Study Area
Philly_blockgroup <- st_read("Dataset/Philly_blockgroup/Philly_blockgroup.shp") %>%
st_transform('EPSG:2272')
# philly bounds
philly_bounds <- st_union(Philly_blockgroup)
studyarea <- st_read("Dataset/studyarea/StudyArea.shp") %>%
st_transform('EPSG:2272')
studyarea_blockgroup <- st_read("Dataset/studyarea/StudyArea_blockgroup_tract.shp") %>%
st_transform('EPSG:2272')
studyarea_north <- st_read("Dataset/studyarea-sub/studyarea_north.shp") %>%
st_transform('EPSG:2272')
studyarea_south <- st_read("Dataset/studyarea-sub/studyarea_south.shp") %>%
st_transform('EPSG:2272')
I_676 <- st_read("Dataset/Highways/I_676.shp") %>%
st_transform('EPSG:2272')
discontinuity <- st_read("Dataset/studyarea-sub/discontinuity.shp") %>%
st_transform('EPSG:2272')
Chinatown_Stitch <- st_read("Dataset/Chinatown_Stitch/Chinatown_Stitch.shp") %>%
st_transform('EPSG:2272')
property_original <- st_read("Dataset/original_property_studyarea.geojson") %>%
st_transform('EPSG:2272')
property_north <- property_original %>%
st_intersection(studyarea_north)
property_south <- property_original %>%
st_intersection(studyarea_south)
# philly hydrology for mapping (bounded by philly_bounds; source: https://opendataphilly.org/datasets/hydrology/)
hydro <- st_read("https://services.arcgis.com/fLeGjb7u4uXqeF9q/arcgis/rest/services/Hydrographic_Features_Poly/FeatureServer/1/query?outFields=*&where=1%3D1&f=geojson") %>%
st_transform(crs = 'EPSG:2272') %>%
st_intersection(philly_bounds)
philly_highways <- st_read("Dataset/Highways/Casestudy_Highways.shp") %>%
st_transform('EPSG:2272')Three study areas are used in the analysis to reveal the effects of I-676 on the land market of the Chinatown region.
- Referable study area (Philadelphia): to analyse the impact of different highways on the surrounding land markets in the context of the city.
- Main study area (10 block groups): to figure out the correlation between I-676 and property sales price and make future predictions based on three potential scenarios.
- Compared study area (north and south sides): to investigate the different situations of the Chinatown region under the construction of I-676.
StudyArea_plot1 <- ggplot() +
geom_sf(data = Philly_blockgroup, fill = "transparent", color = "grey90", linewidth = .2) +
geom_sf(data = studyarea, fill = "#eb5600", color = "#eb5600") +
geom_sf(data = hydro, fill = "#DFF3E3", color = "transparent") +
geom_sf(data = philly_bounds, fill = "transparent", color = "grey60", linewidth = 1, linetype = "dashed") +
theme_void() +
labs(title = "Three Study Areas:",
subtitle = "Philadelphia | 10 Block Groups around the Chinatown Stitch | North and South Sides",
caption = "Source: ACS Census Data and OPA Data.") +
theme(plot.title = element_text(size = 16, face = "bold",margin = margin(b = 6)),
plot.subtitle = element_text(margin = margin(b = 10)),
plot.caption = element_text(hjust = 0, margin = margin(t = 10)))
StudyArea_plot2 <- ggplot() +
geom_sf(data = studyarea_north, fill = alpha("#eb5600", .1), color = "transparent") +
geom_sf(data = studyarea_south, fill = alpha("#1a9988", .1), color = "transparent") +
# geom_sf(data = discontinuity, fill = "black", color = "transparent") +
geom_sf(data = Chinatown_Stitch, fill = "grey", color = "transparent", linetype = "dashed", linewidth = 1) +
geom_sf(data = studyarea_blockgroup, fill = "transparent", color = "grey", linewidth = .8, linetype = "dashed") +
geom_sf(data = property_north, color = "#eb5600", size = .6) +
geom_sf(data = property_south, color = "#1a9988", size = .6) +
geom_sf(data = studyarea, fill = "transparent", color = "grey60", linewidth = 1.2, linetype = "dashed") +
theme_void()
StudyArea_plot1 | StudyArea_plot2
studyarea_4326 <- st_transform(studyarea, crs = 4326)
bbox <- as.list(st_bbox(studyarea_4326))
ChinatownStitch_4326 <- st_read("Dataset/Chinatown_Stitch/Chinatown_Stitch.shp") %>%
st_transform('EPSG:4326')
I676_4326 <- st_transform(I_676, crs = 4326)
landuse_4326 <- st_read("Dataset/landuse_clip/Land_Use_ClipLayer.shp") %>%
st_transform('EPSG:4326')
landuse_4326_plot <- landuse_4326 %>%
filter(C_DIG2DESC != 51 & C_DIG2DESC != 71)
landuse_park_ing <- landuse_4326 %>%
filter(OBJECTID_1 == 514214)
park_4326 <- st_read("Dataset/ParksModified/Parks_within_1km.shp") %>%
st_transform('EPSG:4326')
# nhoods_4326 <-
# st_read("DataWrangling/data/philadelphia-neighborhoods.geojson") %>%
# # st_read("data/philadelphia-neighborhoods.geojson") %>%
# st_transform('EPSG:4326')
Chinatown_Callowhill <- nhoods_4326 %>%
filter(NAME %in% c("CHINATOWN", "CALLOWHILL"))In the vision of a city park in the future, the Chinatown Stitch will bring vitality to local residents and visitors along with other green spaces. Connected with the Stitch, the Rail Park in the north (Callowhill neighborhood) is still in construction and will make an impact years later. With a foundation of cultural and commercial resources, the southern part (Chinatown neighborhood) will provide services to the surrounding area, including the city center of Philadelphia.
UseCase <- leaflet() %>%
addProviderTiles(providers$CartoDB.Positron) %>%
addPolygons(data = studyarea_4326,
color = "black", # Black border
weight = 4, # Border thickness
dashArray = "8,12", # Dashed line pattern (5px on, 5px off)
fill = FALSE) %>%
addPolygons(data = landuse_4326_plot,
color = "grey",
weight = 0.8,
fillColor = "#DFF3E3",
fillOpacity = 0.4) %>%
addPolygons(data = Chinatown_Callowhill,
color = "#eb5600",
weight = 1,
opacity = 0.5,
dashArray = "5,5",
fillColor = "#eb5600",
fillOpacity = 0.1) %>%
addPolygons(data = park_4326,
color = "#1a9988",
weight = 1,
fillOpacity = 0.6,
fillColor = "#1a9988") %>%
addPolygons(data = landuse_park_ing,
color = "#1a9988",
weight = 2,
opacity = 0.8,
fill = FALSE) %>%
addPolylines(data = I676_4326,
color = "#eb5600",
opacity = 1,
weight = 2) %>%
addPolygons(data = ChinatownStitch_4326,
color = "#eb5600",
weight = 2,
opacity = 1,
fillColor = "#1a9988",
fillOpacity = 0.8
# fill = alpha("#1a9988", 0.5)
) %>%
fitBounds(
lng1 = bbox$xmin,
lat1 = bbox$ymin,
lng2 = bbox$xmax,
lat2 = bbox$ymax
)
div(class = "center-leaflet", UseCase)
|
|
| Callowhill | Chinatown |
1.3 Methodology
To achieve the goal of a data-rich story map, the methodology is structured around a robust model construction process. By incorporating illustrative data visualizations throughout the data analysis, the story map features engaging maps and compelling narratives.
TODO: include description of qualitative analysis
2 Data Wrangling
2.1 Property Data
# property <- st_read("DataWrangling/data/opa_properties_public.geojson") %>%
# st_transform('EPSG:2272')
#
# property_studyarea <- st_intersection(property, studyarea)
#
# st_write(property_studyarea %>% st_transform('EPSG:3857'), "Dataset/original_property_studyarea.geojson", driver = "GeoJSON")
property_studyarea <- st_read("Dataset/original_property_studyarea.geojson") %>%
st_transform('EPSG: 2272')
landuse <- st_read("Dataset/landuse_clip/Land_Use_ClipLayer.shp") %>%
st_transform('EPSG:2272')
landuse_rename <- landuse %>%
mutate(landuse =
case_when(
C_DIG2DESC == 11 ~ "Residential Low",
C_DIG2DESC == 12 ~ "Residential Medium",
C_DIG2DESC == 13 ~ "Residential High",
C_DIG2DESC == 21 ~ "Commercial Consumer",
C_DIG2DESC == 22 ~ "Commercial Business/Professional",
C_DIG2DESC == 23 ~ "Commercial Mixed Residential",
C_DIG2DESC == 31 ~ "Industrial",
C_DIG2DESC == 41 ~ "Civic/Institution",
C_DIG2DESC == 51 ~ "Transportation",
C_DIG2DESC == 61 ~ "Culture/Amusement",
C_DIG2DESC == 62 ~ "Active Recreation",
C_DIG2DESC == 71 ~ "Park/Open Space",
C_DIG2DESC == 72 ~ "Cemetery",
C_DIG2DESC == 81 ~ "Water",
C_DIG2DESC == 91 ~ "Vacant",
C_DIG2DESC == 92 ~ "Other/Unknown"
))
# philly properties (from Sijia)
# from the last 5 years, inflation-adjusted prices, total area > 100 sq ft, prices > 30,000, outliers above 5 SD from the mean were removed
# philly_properties <- st_read("data/property_philly_250318.geojson")2.2 Census Data
2.3 Neighborhood Context
nhoods <-
st_read("Dataset/DataWrangle/philadelphia-neighborhoods.geojson") %>%
st_transform('EPSG:2272')
park <- st_read("Dataset/ParksModified/Parks_within_1km.shp") %>%
st_transform('EPSG:2272')
metro <-
st_read("https://opendata.arcgis.com/api/v3/datasets/af52d74b872045d0abb4a6bbbb249453_0/downloads/data?format=geojson&spatialRefId=4326") %>%
st_transform('EPSG:2272') %>%
mutate(Type = "metro")
city_hall <- metro[metro$Station == 'City Hall', 6]
trolley <-
st_read("https://opendata.arcgis.com/api/v3/datasets/dd2afb618d804100867dfe0669383159_0/downloads/data?format=geojson&spatialRefId=4326") %>%
st_transform('EPSG:2272')
trolley_renamed <- trolley %>%
rename(Station = StopName,
Route = LineAbbr,
Longitude = Lon,
Latitude = Lat) %>%
mutate(Type = "trolley")
# Combine both datasets into one
transit <- bind_rows(metro, trolley_renamed)
school <-
st_read("https://opendata.arcgis.com/datasets/d46a7e59e2c246c891fbee778759717e_0.geojson") %>%
st_transform('EPSG:2272')
hospital <-
st_read("Dataset/DataWrangle/DOH_Hospitals202311.geojson") %>%
st_transform('EPSG:2272') %>%
st_filter(st_union(Philly_blockgroup))
water <-
st_read("https://services.arcgis.com/fLeGjb7u4uXqeF9q/arcgis/rest/services/Hydrographic_Features_Poly/FeatureServer/1/query?outFields=*&where=1%3D1&f=geojson") %>%
st_transform('EPSG:2272')
bike_network <-
st_read("Dataset/DataWrangle/Bike_Network.geojson") %>%
st_transform('EPSG:2272')
phillyCrimes <-
st_read("Dataset/DataWrangle/phillyCrimes.geojson") %>%
st_transform('EPSG:2272')
# LPSS_PER1000: Number of low-produce supply stores per 1,000 people
# HPSS_PER1000: Number of high-produce supply stores per 1,000 people
retail <-
st_read("https://opendata.arcgis.com/datasets/53b8a1c653a74c92b2de23a5d7bf04a0_0.geojson") %>%
st_transform('EPSG:2272')calculate_nearest_distance <- function(set_points, other_layer) {
nearest_idx <- st_nearest_feature(set_points, other_layer)
st_distance(set_points, other_layer[nearest_idx, ], by_element = TRUE) %>% as.numeric()
}
Luming_property <- property_studyarea %>%
dplyr::select(objectid, geometry, sale_price) %>%
rename(orig_objectid = objectid)
Luming_property <- Luming_property %>%
mutate(distance_to_city_hall = st_distance(., city_hall) %>% as.numeric()) %>%
mutate(
distance_to_nearest_transit = calculate_nearest_distance(geometry, transit),
distance_to_nearest_hospital = calculate_nearest_distance(geometry, hospital),
distance_to_nearest_school = calculate_nearest_distance(geometry, school),
distance_to_nearest_park = calculate_nearest_distance(geometry, park),
distance_to_nearest_water = calculate_nearest_distance(geometry, water),
distance_to_nearest_bikelane = calculate_nearest_distance(geometry, bike_network),
distance_to_I676 = calculate_nearest_distance(geometry, I_676)
) %>%
st_join(nhoods["NAME"], left = TRUE) %>%
rename(nhoods_name = NAME) %>%
st_join(landuse_rename["landuse"], left = TRUE) %>%
st_join(retail[, c("LPSS_PER1000", "HPSS_PER1000")], left = TRUE)
Luming_property <- Luming_property %>%
mutate(
crime_nn1 = nn_function(st_coordinates(Luming_property),
st_coordinates(phillyCrimes), k = 1),
crime_nn2 = nn_function(st_coordinates(Luming_property),
st_coordinates(phillyCrimes), k = 2),
crime_nn3 = nn_function(st_coordinates(Luming_property),
st_coordinates(phillyCrimes), k = 3),
crime_nn4 = nn_function(st_coordinates(Luming_property),
st_coordinates(phillyCrimes), k = 4),
crime_nn5 = nn_function(st_coordinates(Luming_property),
st_coordinates(phillyCrimes), k = 5))2.5 POI Data
3 Highway Effects Analysis
3.1 Qualitative Research
property_EDA <- st_read("Dataset/property_sijia_eda.geojson") %>%
st_transform('EPSG:2272')
property_EDA_update <- property_EDA %>%
dplyr::select(-sale_price.y) %>%
rename(sale_price = sale_price.x) %>%
mutate(log_sale_price = ifelse(!is.na(sale_price) & sale_price == 0,
log(sale_price + 1),
log(sale_price)))
property_EDA_update <-
rbind(
property_EDA_update %>% st_intersection(studyarea_north["geometry"]) %>%
mutate(I676 = "north"),
property_EDA_update %>% st_intersection(studyarea_south["geometry"]) %>%
mutate(I676 = "south")
)3.2 Philadelphia City Scale Analysis
Before jumping into modeling, we first explored the relationship between distance to highway and property prices. We used property data from OPA Philadelphia that was cleaned to include only the data from the last five years (Jan. 1st, 2020 to Dec. 31st, 2024) with prices adjusted for inflation after removing extreme outliers and improbable values, and keeping properties with total area of over 100 square feet. Looking at properties (of all kinds) within 500 m of Phildelphia’s main highways – I-95 on the East, I-76 on the West, US-1 in the North, and I-676 through the city – we saw the following trends in property prices.
buff500 <- 500 * 3.28084
hiway_500buff <- st_union(st_buffer(philly_highways, buff500))
# adding distance to highway and log price variables
# properties500 <- philly_properties[hiway_500buff,] %>%
# mutate(price_perTLA = adjusted_sale_price / total_livable_area,
# price_perTA = adjusted_sale_price / total_area,
# log_price = log(adjusted_sale_price),
# log_price_perTLA = log(price_perTLA),
# log_price_perTA = log(price_perTA),
# dist_to_US001 = calculate_nearest_distance(geometry,
# philly_highways %>%
# filter(ST_RT_NO == "0001")),
# dist_to_I076 = calculate_nearest_distance(geometry,
# philly_highways %>%
# filter(ST_RT_NO == "0076")),
# dist_to_I095 = calculate_nearest_distance(geometry,
# philly_highways %>%
# filter(ST_RT_NO == "0095")),
# dist_to_I676 = calculate_nearest_distance(geometry,
# philly_highways %>%
# filter(ST_RT_NO == "0676")),
# dist_to_US001m = dist_to_US001 * 0.3048,
# dist_to_I076m = dist_to_I076 * 0.3048,
# dist_to_I095m = dist_to_I095 * 0.3048,
# dist_to_I676m = dist_to_I676 * 0.3048)
#
# prop500_closest <- properties500 %>%
# select(matches("objectid|dist_to")) %>%
# pivot_longer(cols = 2:5,
# names_to = "highway",
# values_to = "distance") %>%
# group_by(objectid) %>%
# summarise(dist_to_closest_highway = min(distance, na.rm = T)) %>%
# ungroup() %>%
# mutate(dist_to_closest_highwaym = dist_to_closest_highway * 0.3048)
#
# # join closest highway
# prop500 <- left_join(properties500,
# prop500_closest %>% st_drop_geometry(),
# by = "objectid") %>%
# mutate(closest_highway = case_when(
# dist_to_closest_highway == dist_to_US001 ~ "US-1",
# dist_to_closest_highway == dist_to_I076 ~ "I-76",
# dist_to_closest_highway == dist_to_I095 ~ "I-95",
# dist_to_closest_highway == dist_to_I676 ~ "I-676",
# .default = NA),
# closest_highway = factor(closest_highway, levels = c("US-1", "I-76", "I-676", "I-95")),
# highway_type = case_when(closest_highway %in% c("US-1","I-676") ~ "through neighborhood",
# closest_highway %in% c("I-95","I-76") ~ "along waterway",
# .default = NA))
# save output and read in the prefiltered to save time
# st_write(prop500, "data/philly_prop500_dst2hghwy_250325.geojson")
# read in pre-saved file
prop500 <- st_read("Dataset/PhillyPropertiesNearHighways/philly_prop500_dst2hghwy_250325.geojson") %>%
mutate(closest_highway = factor(closest_highway, levels = c("US-1", "I-76", "I-676", "I-95")))# map of properties within 500m of highway cored by which highway they're closest to
ggplot() +
geom_sf(data = philly_bounds, fill = "#c9b887", color = "grey") +
geom_sf(data = hydro, fill = "#7d97c7", color = "transparent") +
geom_sf(data = park, fill = "#6b9169", color = "transparent") +
geom_sf(data = hiway_500buff,
color = "transparent", fill = "grey", alpha = 0.4) +
geom_sf(data = prop500,
aes(color = closest_highway), size = 0.3,
show.legend = F) +
geom_sf(data = philly_highways,
color = "black") +
# add custom colors
scale_color_manual(values = c("US-1" = "#1a9988",
"I-76" = "#eb5600",
"I-95" = "#eba075",
"I-676" = "#7fe3d6")) +
# labels for highway names
geom_text(aes(label = "US-1",
x = 2700000,
y = 256453.2),
color = "#1a9988") +
geom_text(aes(label = "I-76",
x = 2680000,
y = 235000),
color = "#eb5600") +
geom_text(aes(label = "I-95",
x = 2705200,
y = 235000),
color = "#eba075") +
geom_text(aes(label = "I-676",
x = 2694000,
y = 243000),
color = "#7fe3d6") +
labs(title = "Properties within 500m of Highways in Philly",
subtitle = "US-1, I-76, I-95, and I-676",
color = "Closest Highway") +
theme_void()
prop500_faceted <-
ggplot(data = prop500,
aes(x = log_price_perTA, color = closest_highway, fill = closest_highway)) +
geom_histogram(bins = 50, show.legend = F) +
facet_wrap(~closest_highway, scales = "free_y") +
scale_color_manual(values = c("US-1" = "#1a9988",
"I-76" = "#eb5600",
"I-95" = "#eba075",
"I-676" = "#7fe3d6")) +
scale_fill_manual(values = c("US-1" = "#1a9988",
"I-76" = "#eb5600",
"I-95" = "#eba075",
"I-676" = "#7fe3d6")) +
labs(title = "Distribution of Property Sale Price",
subtitle = "Split by Closest Highway",
x = "Log Sale Price (per Total Area)",
y = "Count") +
theme_minimal()
prop500_faceted
# median sale prices to talk about in the text
med_price_us1 <- round(median(prop500 %>%
filter(closest_highway == "US-1") %>%
pull(adjusted_sale_price)))
med_price_i76 <- round(median(prop500 %>%
filter(closest_highway == "I-76") %>%
pull(adjusted_sale_price)))
med_price_i95 <- round(median(prop500 %>%
filter(closest_highway == "I-95") %>%
pull(adjusted_sale_price)))
med_price_676 <- round(median(prop500 %>%
filter(closest_highway == "I-676") %>%
pull(adjusted_sale_price)))We see that the distribution of log sale prices of properties within 500m of highways in Philadelphia generally follow a normal distribution. Though there aren’t as many properties near I-676 as other there are near the other highways, properties are most expensive here with a median property price of $972,362. Properties near I-95 and I-76 are among a similar range of prices with median property prices being $354,750 and $315,203, respectively, though there are almost five times as many properties near I-95 compared to I-76. Finally, there are many properties within 500m of US-1, but the median property price was lowest compared to other highways at $234,847.
# simple scatter of price (as log price per total area in sq ft) and dist2closesthighway
cor_us1 <- round(cor(prop500 %>%
filter(grepl("US", closest_highway)) %>%
pull(log_price_perTA),
prop500 %>%
filter(grepl("US", closest_highway)) %>%
pull(dist_to_closest_highwaym)), 3)
cor_i76 <- round(cor(prop500 %>%
filter(grepl("I-76", closest_highway)) %>%
pull(log_price_perTA),
prop500 %>%
filter(grepl("I-76", closest_highway)) %>%
pull(dist_to_closest_highwaym)), 3)
cor_i95 <- round(cor(prop500 %>%
filter(grepl("I-95", closest_highway)) %>%
pull(log_price_perTA),
prop500 %>%
filter(grepl("I-95", closest_highway)) %>%
pull(dist_to_closest_highwaym)), 3)
cor_i676 <- round(cor(prop500 %>%
filter(grepl("I-676", closest_highway)) %>%
pull(log_price_perTA),
prop500 %>%
filter(grepl("I-676", closest_highway)) %>%
pull(dist_to_closest_highwaym)), 3)
scatter_4highways <-
ggplot(data = prop500,
aes(x = dist_to_closest_highwaym, y = log_price_perTA,
color = closest_highway, fill = closest_highway)) +
geom_point(alpha = .2, size = 0.3, show.legend = F) +
geom_smooth(method = "lm", size = 1.2, show.legend = F) +
scale_color_manual(values = c("US-1" = "#1a9988",
"I-76" = "#eb5600",
"I-95" = "#eba075",
"I-676" = "#7fe3d6")) +
scale_fill_manual(values = c("US-1" = "#1a9988",
"I-76" = "#eb5600",
"I-95" = "#eba075",
"I-676" = "#7fe3d6")) +
geom_text(aes(label = paste("US-1:", cor_us1),
x = 150,
y = 1.25),
color = "#1a9988",
show.legend = F) +
geom_text(aes(label = paste("I-76:", cor_i76),
x = 350,
y = 1.25),
color = "#eb5600",
show.legend = F) +
geom_text(aes(label = paste("I-95:", cor_i95),
x = 350,
y = 8.75),
color = "#eba075",
show.legend = F) +
geom_text(aes(label = paste("I-676:", cor_i676),
x = 150,
y = 8.75),
color = "#7fe3d6",
show.legend = F) +
labs(title = "Property Price (log price per TA) as a function of Distance to Closest Highway",
subtitle = "Properties within 500m of highways",
x = "Distance to Closest Highway (m)",
y = "Property Sale Price (log price per TA)") +
theme_minimal()
scatter_4highways
From this scatter plot, we see that properties within 500m of I-676 and I-76 follow the expected pattern of increasing property prices as one gets further from the highway (folks want to avoid the noise and air pollution from the highway). The correlation between distance to closest highway (in meters) and Property sale price (log-transformed price per total area) is 0.147 and 0.107 for properties near I-676 and I-76, respectively, meaning for every additional meter away from the highway, property price per square foot of total area increase by 15.8% and 11.3%.
3.3 Study Region Scale Analysis
3.3.1 Basic Geography
park_clipped <- st_intersection(park, studyarea)
park_valid <- st_make_valid(park)
transit_valid <- st_make_valid(transit)
city_hall_valid <- st_make_valid(city_hall)
school_valid <- st_make_valid(school)
water_valid <- st_make_valid(water)
bike_network_valid <- st_make_valid(bike_network)
studyarea_buffer <- st_buffer(studyarea, dist = 1000)
# Clip each dataset to the study area
recreation_clipped <- st_intersection(park, studyarea_buffer)
transit_clipped <- st_intersection(transit_valid, studyarea_buffer)
city_hall_clipped <- st_intersection(city_hall_valid, studyarea_buffer)
school_clipped <- st_intersection(school_valid, studyarea_buffer)
water_clipped <- st_intersection(water_valid, studyarea_buffer)
bike_network_clipped <- st_intersection(bike_network_valid, studyarea_buffer)
landuse_transportation <- landuse_rename[landuse_rename$landuse == "Transportation", ]
property_vacant <- property_EDA %>%
filter(category_code_description %in% c("VACANT LAND","SINGLE FAMILY", "VACANT LAND - NON-RESIDENTIAL")) %>%
mutate(category_code_description = case_when(
category_code_description == "VACANT LAND - NON-RESIDENTIAL" ~ "VACANT LAND",
TRUE ~ category_code_description
))landuse_colors <- c(
"Residential Low" = "#FFFFB3",
"Residential Medium" = "#FFEB8B",
"Residential High" = "#FFCC80",
"Commercial Consumer" = "#FFB3B3",
"Commercial Business/Professional" = "#FF9980",
"Commercial Mixed Residential" = "#FF6666",
"Industrial" = "#D6A3FF",
"Civic/Institution" = "#66B3FF",
"Transportation" = "gray95",
"Culture/Amusement" = "#C1FFC1",
"Active Recreation" = "#66FFD9",
"Park/Open Space" = "#99FF66",
"Cemetery" = "#FF9999",
"Water" = "#99CCFF",
"Vacant" = "#D3D3D3",
"Other/Unknown" = "gray25"
)
BasicGeo_plot1 <- ggplot(data = landuse_rename %>%
filter(!is.na(landuse) & landuse != "Park/Open Space")) +
geom_sf(aes(fill = landuse), color = NA) +
geom_sf(data = park_clipped, aes(fill = "Park"), color = NA, alpha = 1) + # Clipped park layer
scale_fill_manual(values = c(landuse_colors, "Park" = "#99FF66"), na.value = "white") +
labs(title = "Land Use",
subtitle = "Properties functions in Chinatown",
fill = "Category") +
theme_void() +
theme(legend.position = "right",
plot.title = element_text(size = 16, face = "bold"),
plot.subtitle = element_text(size = 10),
legend.key.size = unit(0.5, "cm"),
legend.text = element_text(size = 8))
BasicGeo_plot2 <- ggplot() +
geom_sf(data = landuse, fill = "white", color = "gray90", size = 0.05) +
geom_sf(data = recreation_clipped, aes(fill = "Park"), color = NA, alpha = 1) +
geom_sf(data = landuse_transportation, aes(fill = "Transportation"), color = NA, alpha = 0.5) +
geom_sf(data = transit_clipped, aes(color = "Transit"), size = 2) +
geom_sf(data = school_clipped, aes(color = "School"), size = 2) +
geom_sf(data = bike_network_clipped, aes(color = "Bike Network"), size = 1) +
scale_color_manual(values = c("Transit" = "blue", "School" = "orange", "Bike Network" = "purple"), name = NULL) +
scale_fill_manual(values = c("Park" = "green","Transportation" = "gray70"), name = NULL) +
labs(
title = "Amenities",
subtitle = "<span style='color:orange;'>Schools</span>, <span style='color:blue;'>Transit Stations</span>, <span style='color:green;'>Parks</span>, and <span style='color:purple;'>Bike Lanes</span>"
) +
theme_void() +
theme(
legend.position = "none",
plot.title = element_text(size = 16, face = "bold"),
plot.subtitle = element_markdown(size = 10, hjust = 0),
legend.key.size = unit(0.5, "cm"),
legend.text = element_text(size = 8)
)
BasicGeo_plot3 <- ggplot() +
geom_sf(data = landuse, fill = "white", color = "gray85", size = 0.05) +
geom_sf(data = studyarea, fill = "transparent", color = "grey", linetype = "dashed", linewidth = 2) +
geom_sf(data = discontinuity, fill = "#eb5600", color = "transparent") +
geom_sf(data = Chinatown_Stitch, fill = "#1a9988", alpha = 0.8) +
geom_sf(data = property_vacant, aes(color = category_code_description), size = 1) +
scale_colour_manual(values = c("black", "#B67352")) +
labs(title="Potential Lands",
subtitle = glue("<span style='color:#1a1a1a;'>Single Family & </span><span style='color:#B67352;'>Vacant Land</span>"),
# caption = "Figure "
) +
theme_void() +
theme(legend.position = "none",
plot.title = element_text(size = 16, face = "bold"),
plot.subtitle = ggtext::element_markdown(size = 10))
BasicGeo_plot1
BasicGeo_plot2 | BasicGeo_plot3
3.3.2 Sales Price Change
|
|
3.3.3 I-676 Effects
3.3.4 Discontinuity Effects
property_discontinutiy <- property_EDA_update %>%
mutate(distance_to_highway = case_when(
I676 == "north" ~ distance_to_I676,
I676 == "south" ~ -distance_to_I676
))
discontinuity_plot <- property_discontinutiy %>%
st_drop_geometry() %>%
filter(sale_price > 10 & sale_price < 1e6) %>%
group_by(distance_to_highway, I676) %>%
summarise(log_sale_price = mean(log_sale_price, na.rm = TRUE), .groups = "drop")ggplot(discontinuity_plot, aes(x = distance_to_highway, y = log_sale_price, color = I676)) +
geom_point(size = 0.5) +
geom_smooth(method = "lm", se = FALSE, size = 1.2) +
geom_vline(xintercept = 0, color = "black", size = 2) +
scale_colour_manual(values = c("#eb5600", "#1a9988")) +
theme_minimal() +
theme(legend.position = "None",
plot.title = element_text(size = 16, face = "bold"),
plot.subtitle = ggtext::element_markdown(size = 12),
plot.caption = element_text(hjust = 0)) +
labs(title = 'Logged Sale Price as a Function of Distance to the Highway',
subtitle = glue("<span style='color:#1a9988;'>South Side</span> vs. <span style='color:#eb5600;'>North Side</span>"),
# caption = "Figure 4.1",
# x = "South Side of the Highway I-676 North Side of the Highway",
x = "Distance to the Highyway (ft) ",
y = "Log-transformed Sale Price") +
geom_label(aes(x = 0, y = 8,
label = "I-676"),
fill = "black", color = "white", fontface = "bold", size = 5)
2.4 Social Survey